With the development of telecommunications techniques, network bandwidth is increased dramatically. Therefore, a large amount of websites and client software providing download resources emerge. These websites and client software classify the download resources into different categories and present them to users. Users may download a download resource according to their requirements. The download resources may include software, video, audio, picture, text, etc. In order to facilitate the selection of the user, besides allowing the user to submit downloading request initiatively, the websites and client software also provide a download resource recommendation function, i.e., push some high quality download resources, such as hot films, hot games, frequently-used software, to a webpage or a client, so as to present the recommended download resources to the user.
In a conventional download resource recommendation method, the hot degree of a download resource is determined according to a download record of the download resource (the more times that the download resource is downloaded, the higher the hot degree). Then the download resources are sorted. Top N download resources (top N download resources whose hot degrees rank in first N, N may be determined according to a practical requirement) are recommended to the user. However, the download record merely statistically reflects interests of all users. Therefore, the user may be not interested in the download resource recommended to the user.
In a conventional improved downloading recommendation method, a score table (score matrix) is created first according to a score of each download resource given by each user. Then, according to a similarity degree between scores of different download resources, a target user group similar to a target user (a user that the download resource is to be recommended to) is determined. According to a whole interest of the group (e.g., scores of the download resources provided by users in the target user group), a download resource is recommended to the user.
Although this improved download resource recommendation method increases the correlation degree between the download resource and the user, it is hard to obtain the score of the download resource provided by the user (most users do not give a score after downloading the resource). In addition, the interest of the target user on the download resource is determined merely according to the overall interest of the target user group, which makes the target user and the download resource less correlated. Thus, the accuracy ratio of the download resource recommendation method is not high.